因子型变量_数据框_函数式编程purrr包

因子型变量_数据框_函数式编程purrr包#

什么是因子#

因子是把数据进行分类并标记为不同层级(level，有时候也翻译成因子水平，我个人觉得翻译为层级，更接近它的特性，因此，我都会用层级来描述)的数据对象，他们可以存储字符串和整数。因子类型有三个属性：

存储类别的数据类型
离散变量
因子的层级是有限的，只能取因子层级中的值或缺失(NA)

创建因子#

因子层级会自动按照字符串的字母顺序排序，比如high low medium。也可以用levels=c()指定顺序
不属于因子层级中的值, 会被当作缺省值NA

library(tidyverse)

── Attaching core tidyverse packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.0     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     

── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(palmerpenguins)

income <- c("low", "high", "medium", "medium", "low", "high",  "high")
factor(income)

low
high
medium
medium
low
high
high

Levels:

'high'
'low'
'medium'

## 指定顺序
factor(income, levels=c("low", "high", "medium"))

low
high
medium
medium
low
high
high

Levels:

'low'
'high'
'medium'

factor(income, levels=c("low", "high"))

low
high
<NA>
<NA>
low
high
high

Levels:

'low'
'high'

相比较字符串而言，因子类型更容易处理，因此很多函数会自动的将字符串转换为因子来处理，但事实上，这也会造成，不想当做因子的却又当做了因子的情形

最典型的是在R 4.0之前，data.frame()中stringsAsFactors选项，默认将字符串类型转换为因子类型，但这个默认也带来一些不方便，因此在R 4.0之后取消了这个默认。

在tidyverse集合里，有专门处理因子的宏包forcats

library(forcats)

调整因子顺序#

因子层级默认是按照字母顺序排序
fct_relevel()指定因子顺序
- after=Inf将某个因子移到最后面
fct_inorder() 按照字符串第一次出现的次序
fct_reorder() 按照其他变量的升序排序 .fun=指定函数
fct_rev() 按照因子层级的逆序排序
fct_infreq()按照因子频率排序，从大到小
- fct_rev(fct_infreq())按照因子频率排序，从小到大

income <- c("low", "high", "medium", "medium", "low", "high",  "high")
x <- factor(income)
x
# 指定顺序
x %>% fct_relevel(c("high", "medium", "low"))
x %>% fct_relevel("medium")
x %>% fct_relevel("medium", after=Inf)

# 按照字符串第一次出现的次序
x %>% fct_inorder()

low
high
medium
medium
low
high
high

Levels:

'high'
'low'
'medium'

low
high
medium
medium
low
high
high

Levels:

'high'
'medium'
'low'

low
high
medium
medium
low
high
high

Levels:

'medium'
'high'
'low'

low
high
medium
medium
low
high
high

Levels:

'high'
'low'
'medium'

low
high
medium
medium
low
high
high

Levels:

'low'
'high'
'medium'

# 按照其他变量的中位数的升序排序
x %>% fct_reorder(c(1:7), .fun=median)

low
high
medium
medium
low
high
high

Levels:

'low'
'medium'
'high'

应用#

调整因子层级有什么用呢？

这个功能在ggplot可视化中调整分类变量的顺序非常方便

d <- tibble(
  x = c("a","a", "b", "b", "c", "c"),
  y = c(2, 2, 1, 5,  0, 3)
  
)
d

A tibble: 6 × 2
x	y
<chr>	<dbl>
a	2
a	2
b	1
b	5
c	0
c	3

d %>% 
  ggplot(aes(x = x, y = y))+
  geom_point()

../_images/cbe39ab5f9de900e61f6fe7a4b68f6bf35a4b81590e68e7756baefa36a4e680f.png

`fct_reorder()`#

fct_reorder(x, y, .fun=median)可以让x的顺序按照x中每个分类变量对应y值的中位数升序排序
.desc = TRUE颠倒顺序

d %>% 
  ggplot(aes(x = fct_reorder(x, y, .fun=median), y = y)) + 
  geom_point()

../_images/77096e0600a4ff824f03552c707f47bf77f7d66ea8358f9d93f5a9b040c08570.png

d %>% 
  ggplot(aes(x = fct_reorder(x,y, .fun=median, .desc=TRUE), y = y)) + 
  geom_point()
d %>% 
  mutate(x = fct_reorder(x, y, .fun=median, .desc=TRUE)) %>% 
  ggplot(aes(x = x, y = y)) +
  geom_point()

../_images/8fb79640d77c3990d52119a48025e26bc4671de77c859b9f71a4a07688d9ee3d.png

../_images/c03f0d6b3afe8969621f9d0a9891c945547b7651768ae698bbe6c2c92487ef94.png

`fct_rev()`#

按照因子层级的逆序排序

d %>% 
  mutate(x = fct_rev(x)) %>% 
  ggplot(aes(x, y)) + 
  geom_point()

../_images/d218ebc366d05c3eccdc828e01a318d5f629c176a69f95b989e8c3a2ce1efda7.png

`fct_relevel()`#

d %>% 
  mutate(
    x = fct_relevel(x, c("c", "a", "b"))
  ) %>% 
  ggplot(aes(x, y))+
  geom_point()

../_images/a19a0e0f97af48e0145e2f9eee60ae279fc1df58b47ac281e4ffc81c617e026d.png

可视化中应用#

library(palmerpenguins)

ggplot(penguins, aes(y=species))+
  geom_bar()

../_images/7d2f43225ad91f14c1ad92e93be2550fb8885b6174ed28d75cad322e7a59a732.png

# 按species逆序
ggplot(penguins, aes(y = fct_rev(species)))+
  geom_bar()

../_images/f5450551fb62615110984bcb4a90a09609831f1de738b224f13a7033305f4c62.png

penguins %>% 
  count(species) %>% 
  pull(species)

penguins %>% 
  count(species) %>% 
  mutate(species = fct_relevel(species, c("Chinstrap", "Gentoo", "Adelie"))) %>% 
  pull(species)

# 把Chinstrap移到前面, 其他顺序不变
ggplot(penguins, aes(y = fct_relevel(species, "Chinstrap"))) +
  geom_bar()

Adelie
Chinstrap
Gentoo

Levels:

'Adelie'
'Chinstrap'
'Gentoo'

Adelie
Chinstrap
Gentoo

Levels:

'Chinstrap'
'Gentoo'
'Adelie'

../_images/93d88367de6a165c8b5389cb71344ccbe8f494cedec6c01556267d7d0f260a3a.png

# Use order "Chinstrap", "Gentoo", "Adelie"
ggplot(penguins, aes(y = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie"))) +
  geom_bar()

../_images/0978a42635f978901d0e65c534fbb66d363ecdbe973e251574ee08d1af94e9ef.png

penguins %>% 
  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>% 
  ggplot(aes( y = species))+
  geom_bar()

../_images/4cba8e6539ca855109aec2c7ad16009f1b717d8b13fa671da2b9c4dbf1d01c34.png

# 把Adelie放到最后
ggplot(penguins, aes(y = fct_relevel(species, "Adelie", after=Inf)))+
  geom_bar()

../_images/ca7a4282b98582dff36f36ad99ffa45be678655fce8095d52a1d0fe7596955d4.png

# fct_infreq() 按照因子频率，从小到大
penguins %>% 
  mutate(species = fct_infreq(species)) %>% 
  ggplot(aes(y = species))+
  geom_bar()

penguins %>% 
  mutate(species = fct_rev(fct_infreq(species))) %>% 
  ggplot(aes(y = species))+
  geom_bar()

../_images/bdac55243e5d56be39d15494820699568bdda47221103cf3c4a30c9c7385acb8.png

# n是count()函数产生的结果
penguins %>% 
  count(species) %>% 
  mutate(species = fct_reorder(species, n)) %>% 
  ggplot(aes(n, species))+
  geom_col()

../_images/8afdef0c6f95e0812606f041fc05efdce124fe492d8a9b3a278b1dbb40c7fadd.png

# 画出的2007年美洲人口寿命的柱状图，要求从高到低排序

library(gapminder) # install.packages("gapminder")
gapminder %>% 
  filter(year == 2007 & continent == "Americas") %>% 
  ggplot(aes(x = country, y = lifeExp))+
  geom_col()+
  theme_bw()

../_images/276973e3149460e66c7c97251ad59b66737fa82eb34ebfaa4bf8eecb9de36673.png

# 四个国家人口寿命的变化图
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  ggplot(aes(year, lifeExp)) + 
  geom_line() +
  facet_wrap(vars(country), nrow = 2)

../_images/f1140a25b19860dbd34ead3ace504aa9213a341ae63cbec43014000f9156e130.png

# 按每个国家寿命的中位数
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  mutate(country = fct_reorder(country, lifeExp, .fun=median)) %>% 
  ggplot(aes(year, lifeExp)) +  
  geom_line() +
  facet_wrap(vars(country), nrow = 2)

../_images/a891b08c2f25de25c651f5bb1e09ae389d356e43bba3548f2d58a78f72988033.png

# 按每个国家寿命差（最大值减去最小值）
cha <- function(x){
    max(x) - min(x)
}

gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  mutate(country = fct_reorder(country, lifeExp, .fun=cha)) %>% 
  ggplot(aes(year, lifeExp)) +  
  geom_line() +
  facet_wrap(vars(country), nrow = 2)

../_images/5a5aa172f8146c006cdacbdf78f868cafb4ebf2a6473d4735e785113eed0e456.png

简单数据框#

`tidyverse` 家族#

前面陆续介绍了tidyverse家族，家庭主要成员包括

功能	宏包
有颜值担当	`ggplot2`
数据处理王者	`dplyr`
数据转换专家	`tidyr`
数据载入利器	`readr`
循环加速器	`purrr`
强化数据框	`tibble`
字符串处理	`stringr`
因子处理	`forcats`

人性化的`tibble`#

tibble是用来替换data.frame类型的扩展的数据框
tibble继承了data.frame，是弱类型的。换句话说，tibble是data.frame的子类型
tibble与data.frame有相同的语法，使用起来更方便
tibble更早的检查数据，方便写出更干净、更多富有表现力的代码

tibble对data.frame做了重新的设定：

tibble，不关心输入类型，可存储任意类型，包括list类型
tibble，没有行名设置 row.names
tibble，支持任意的列名
tibble，会自动添加列名
tibble，类型只能回收长度为1的输入
tibble，会懒加载参数，并按顺序运行
tibble，是tbl_df类型

tibble 与 data.frame#

# 传统创建数据框
data.frame(
  a = 1:5,
  b = letters[1:5]
)

A data.frame: 5 × 2
a	b
<int>	<chr>
1	a
2	b
3	c
4	d
5	e

发现，data.frame()会自动将字符串型的变量转换成因子型，如果想保持原来的字符串型，就得

data.frame(
  a = 1:5,
  b = letters[1:5],
  stringsAsFactors = FALSE
)

A data.frame: 5 × 2
a	b
<int>	<chr>
1	a
2	b
3	c
4	d
5	e

Note： - 在R 4.0 后，data.frame() 不会将字符串型变量自动转换成因子型

用tibble创建数据框，不会这么麻烦，输出的就是原来的字符串类型

tibble(
  a = 1:5,
  b = letters[1:5]
)

A tibble: 5 × 2
a	b
<int>	<chr>
1	a
2	b
3	c
4	d
5	e

构建两个有关联的变量，传统的data.frame()会报错

tb <- tibble(
  x = 1:3,
  y = x+2)
tb

df <- data.frame(
  x = 1:3,
  y = x+2
)

A tibble: 3 × 2
x	y
<int>	<dbl>
1	3
2	4
3	5

Warning message in Ops.factor(x, 2):
“‘+’ not meaningful for factors”

Error in data.frame(x = 1:3, y = x + 2): 参数值意味着不同的行数: 3, 7
Traceback:

1. data.frame(x = 1:3, y = x + 2)
2. stop(gettextf("arguments imply differing number of rows: %s", 
 .     paste(unique(nrows), collapse = ", ")), domain = NA)

tibble用缩写定义了7种类型：

类型	含义
`int`	代表`integer`
`dbl`	代表`double`
`chr`	代表`character`向量或字符串
`dttm`	代表日期+时间(`date`+`time`)
`lgl`	代表逻辑判断`TRUE`或者`FALSE`
`fctr`	代表因子类型`factor`
`date`	代表日期`dates`

`tibble`数据操作#

1 创建`tibble`#

tibble() 创建方式和data.frame()一样
tibble::tribble()更加直观

# tibble()创建一个tibble类型的data.frame:
tibble(a = 1:5, b = letters[1:5])
tibble(a = 1:5,
      b = 10:14,
      c = a + b)

A tibble: 5 × 2
a	b
<int>	<chr>
1	a
2	b
3	c
4	d
5	e

A tibble: 5 × 3
a	b	c
<int>	<int>	<int>
1	10	11
2	11	13
3	12	15
4	13	17
5	14	19

# 为了让每列更加直观，也可以tribble()创建，数据量不大的时候
tibble::tribble(
  ~x , ~y, ~z,
  "a", 2, 3.6,
  "b", 1, 8.5
)

A tibble: 2 × 3
x	y	z
<chr>	<dbl>	<dbl>
a	2	3.6
b	1	8.5

2 转换成`tibble`类型#

转换成tibble类型意思就是说，刚开始不是tibble, 现在转换成tibble，包括

data.frame转换成tibble
- as_tibble()
- runif(n, min=0, max=1)生成n个0-1之间的均匀分布随机数
- as.data.frame()转回去
vector转换成tibble
list转换成tibble
- as.list()转回去
matrix转换成tibble
- tibble转回matrix? as.matrix()

# data.frame转换成tibble
t1 <- iris[1:6, 1:4]
class(t1)

as_tibble(t1)

'data.frame'

A tibble: 6 × 4
Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
<dbl>	<dbl>	<dbl>	<dbl>
5.1	3.5	1.4	0.2
4.9	3.0	1.4	0.2
4.7	3.2	1.3	0.2
4.6	3.1	1.5	0.2
5.0	3.6	1.4	0.2
5.4	3.9	1.7	0.4

# vector转型到tibble
x <- as_tibble(1:5)
x

A tibble: 5 × 1
value
<int>
1
2
3
4
5

# 把list转型为tibble
df <- as_tibble(list(x = 1:6, y = runif(6), z= 6:1))
df
# 把tibble再转为list? as.list(df)

A tibble: 6 × 3
x	y	z
<int>	<dbl>	<int>
1	0.002756146	6
2	0.285602915	5
3	0.728152501	4
4	0.423231651	3
5	0.403733124	2
6	0.314811969	1

#  把matrix转型为tibble
m <- matrix(rnorm(15), ncol=5)
m
as_tibble(m)
# tibble转回matrix? as.matrix(df)

A matrix: 3 × 5 of type dbl
1.4621831	-1.0057841	-0.3101543	1.0112314	-1.196249
-0.8665591	-0.9180051	0.4756629	0.4447281	-1.581035
0.9931996	-1.2656851	-0.2639908	-0.1246529	-1.254578

Warning message:
“The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
ℹ Using compatibility `.name_repair`.”

A tibble: 3 × 5
V1	V2	V3	V4	V5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1.4621831	-1.0057841	-0.3101543	1.0112314	-1.196249
-0.8665591	-0.9180051	0.4756629	0.4447281	-1.581035
0.9931996	-1.2656851	-0.2639908	-0.1246529	-1.254578

3 `tibble`简单操作#

增加一列
- add_column()
- mutate()
增加一行
- add_row() 默认加在最后
- .before=n指定加在哪一行

# 构建一个简单的数据框
df <- tibble(
  x = 1:2,
  y = 2:1
)
df

A tibble: 2 × 2
x	y
<int>	<int>
1	2
2	1

# 增加一列
add_column(df, z = 0:1, w = 0)

df %>% 
  mutate(z = 0:1,
         w = 0)

A tibble: 2 × 4
x	y	z	w
<int>	<int>	<int>	<dbl>
1	2	0	0
2	1	1	0

A tibble: 2 × 4
x	y	z	w
<int>	<int>	<int>	<dbl>
1	2	0	0
2	1	1	0

# 增加一行
add_row(df, x = 99, y = 9)

# 在第二行，增加一行
add_row(df, x = 99, y = 9, .before=2)

A tibble: 3 × 2
x	y
<dbl>	<dbl>
1	2
2	1
99	9

A tibble: 3 × 2
x	y
<dbl>	<dbl>
1	2
99	9
2	1

4 有用的函数`lst`#

lst，创建一个list，具有tibble特性的list。

tibble::lst(n = 5, x = runif(n), y = TRUE)

$n

5

$x

0.410855817142874
0.421099713305011
0.420056635513902
0.517503310926259
0.104069613153115

$y

TRUE

5 有用的函数`enframe`#

enframe()将矢量快速创建tibble，创建的tibble只有2列: name和value

enframe(1:3)

A tibble: 3 × 2
name	value
<int>	<int>
1	1
2	2
3	3

enframe(c(a = 5, b = 7, c = 9))

A tibble: 3 × 2
name	value
<chr>	<dbl>
a	5
b	7
c	9

6 有用的函数`deframe`#

deframe()可以看做是enframe()的反操作，把tibble反向转成向量

df <- enframe(c(a = 5, b = 7))
df

deframe(df)

A tibble: 2 × 2
name	value
<chr>	<dbl>
a	5
b	7

a: 5
b: 7

7 读取文件#

read_csv()读取文件时，生成的直接就是tibble

read_csv("./test.csv")

Error: './test.csv' does not exist in current working directory ('/public/home/sll/mybook/content').
Traceback:

1. read_csv("./test.csv")
2. vroom::vroom(file, delim = ",", col_names = col_names, col_types = col_types, 
 .     col_select = {
 .         {
 .             col_select
 .         }
 .     }, id = id, .name_repair = name_repair, skip = skip, n_max = n_max, 
 .     na = na, quote = quote, comment = comment, skip_empty_rows = skip_empty_rows, 
 .     trim_ws = trim_ws, escape_double = TRUE, escape_backslash = FALSE, 
 .     locale = locale, guess_max = guess_max, show_col_types = show_col_types, 
 .     progress = progress, altrep = lazy, num_threads = num_threads)
3. vroom_(file, delim = delim %||% col_types$delim, col_names = col_names, 
 .     col_types = col_types, id = id, skip = skip, col_select = col_select, 
 .     name_repair = .name_repair, na = na, quote = quote, trim_ws = trim_ws, 
 .     escape_double = escape_double, escape_backslash = escape_backslash, 
 .     comment = comment, skip_empty_rows = skip_empty_rows, locale = locale, 
 .     guess_max = guess_max, n_max = n_max, altrep = vroom_altrep(altrep), 
 .     num_threads = num_threads, progress = progress)
4. (function (path, write = FALSE) 
 . {
 .     if (is.raw(path)) {
 .         return(rawConnection(path, "rb"))
 .     }
 .     if (!is.character(path)) {
 .         return(path)
 .     }
 .     if (is_url(path)) {
 .         if (requireNamespace("curl", quietly = TRUE)) {
 .             con <- curl::curl(path)
 .         }
 .         else {
 .             inform("`curl` package not installed, falling back to using `url()`")
 .             con <- url(path)
 .         }
 .         ext <- tolower(tools::file_ext(path))
 .         return(switch(ext, zip = , bz2 = , xz = {
 .             close(con)
 .             stop("Reading from remote `", ext, "` compressed files is not supported,\n", 
 .                 "  download the files locally first.", call. = FALSE)
 .         }, gz = gzcon(con), con))
 .     }
 .     path <- enc2utf8(path)
 .     p <- split_path_ext(basename_utf8(path))
 .     if (write) {
 .         path <- normalizePath_utf8(path, mustWork = FALSE)
 .     }
 .     else {
 .         path <- check_path(path)
 .     }
 .     if (is_installed("archive")) {
 .         formats <- archive_formats(p$extension)
 .         extension <- p$extension
 .         while (is.null(formats) && nzchar(extension)) {
 .             extension <- split_path_ext(extension)$extension
 .             formats <- archive_formats(extension)
 .         }
 .         if (!is.null(formats)) {
 .             p$extension <- extension
 .             if (write) {
 .                 if (is.null(formats[[1]])) {
 .                   return(archive::file_write(path, filter = formats[[2]]))
 .                 }
 .                 return(archive::archive_write(path, p$path, format = formats[[1]], 
 .                   filter = formats[[2]]))
 .             }
 .             if (is.null(formats[[1]])) {
 .                 return(archive::file_read(path, filter = formats[[2]]))
 .             }
 .             return(archive::archive_read(path, format = formats[[1]], 
 .                 filter = formats[[2]]))
 .         }
 .     }
 .     if (!write) {
 .         compression <- detect_compression(path)
 .     }
 .     else {
 .         compression <- NA
 .     }
 .     if (is.na(compression)) {
 .         compression <- tools::file_ext(path)
 .     }
 .     if (write && compression == "zip") {
 .         stop("Can only read from, not write to, .zip", call. = FALSE)
 .     }
 .     switch(compression, gz = gzfile(path, ""), bz2 = bzfile(path, 
 .         ""), xz = xzfile(path, ""), zip = zipfile(path, ""), 
 .         if (!has_trailing_newline(path)) {
 .             file(path)
 .         } else {
 .             path
 .         })
 . })("./test.csv")
5. check_path(path)
6. stop("'", path, "' does not exist", if (!is_absolute_path(path)) {
 .     paste0(" in current working directory ('", getwd(), "')")
 . }, ".", call. = FALSE)

关于行名#

data.frame是支持行名的，但tibble不支持行名，这也是两者不同的地方

has_rownames(df) 判断是否有行名
rownames_to_column(df, var="rowname")把df的行名转换为单独的一列rowname，没行索引了就
rowid_to_column(df, var="rowid")把df的把行索引转换为单独的一列,多一列rowid

# dataframe 支持行名
df <- data.frame(x = 1:3, y = 3:1)

row.names(df) <- LETTERS[1:3]
df

print("判断是否有行名")
has_rownames(df)

# tibble 不支持行名
tb <- tibble(x = 1:3, y = 3:1)

row.names(tb) <- LETTERS[1:3]

A data.frame: 3 × 2
	x	y
	<int>	<int>
A	1	3
B	2	2
C	3	1

[1] "判断是否有行名"

TRUE

Warning message:
“Setting row names on a tibble is deprecated.”

需要注意的：

有时候遇到含有行名的data.frame，转换成tibble后，行名会被丢弃
如果想保留行名，就需要把行名转换成单独的一列

df <- mtcars[1:3, 1:3]
df

# 把行名转换为单独的一列
rownames_to_column(df, var = "myrow")

# 这俩是添加一列，但行名还在
df$rowname <- rownames(df)
df

df %>% 
  mutate(rowname = rownames(df))

A data.frame: 3 × 3
	mpg	cyl	disp
	<dbl>	<dbl>	<dbl>
Mazda RX4	21.0	6	160
Mazda RX4 Wag	21.0	6	160
Datsun 710	22.8	4	108

A data.frame: 3 × 4
myrow	mpg	cyl	disp
<chr>	<dbl>	<dbl>	<dbl>
Mazda RX4	21.0	6	160
Mazda RX4 Wag	21.0	6	160
Datsun 710	22.8	4	108

A data.frame: 3 × 4
	mpg	cyl	disp	rowname
	<dbl>	<dbl>	<dbl>	<chr>
Mazda RX4	21.0	6	160	Mazda RX4
Mazda RX4 Wag	21.0	6	160	Mazda RX4 Wag
Datsun 710	22.8	4	108	Datsun 710

A data.frame: 3 × 4
	mpg	cyl	disp	rowname
	<dbl>	<dbl>	<dbl>	<chr>
Mazda RX4	21.0	6	160	Mazda RX4
Mazda RX4 Wag	21.0	6	160	Mazda RX4 Wag
Datsun 710	22.8	4	108	Datsun 710

# 把行索引转换为单独的一列
rowid_to_column(df, var="rowid")

A data.frame: 3 × 5
rowid	mpg	cyl	disp	rowname
<int>	<dbl>	<dbl>	<dbl>	<chr>
1	21.0	6	160	Mazda RX4
2	21.0	6	160	Mazda RX4 Wag
3	22.8	4	108	Datsun 710

修复列名#

规范的来说，数据框的列名应该是唯一。但现实中代码是人写的，因此可能会稀奇古怪的，所幸的是tibble也提供了人性化的解决方案

.name_repair = "check_unique" 检查列名唯一性，但不做修复（默认）
.name_repair = "minimal"，不检查也不修复，维持现状
.name_repair = "unique" 修复列名，使得列名唯一且不为空
.name_repair = "universal" 修复列名，使得列名唯一且语法可读
make.unique(.x, sep="_")指定修复函数

tibble(x = 1, x = 2)

Error in `tibble()`:
! Column name `x` must not be duplicated.
Use `.name_repair` to specify repair.
Caused by error in `repaired_names()`:
! Names must be unique.
✖ These names are duplicated:
  * "x" at locations 1 and 2.
Traceback:

1. tibble(x = 1, x = 2)
2. tibble_quos(xs, .rows, .name_repair)
3. set_repaired_names(output, repair_hint = TRUE, .name_repair = .name_repair, 
 .     call = call)
4. repaired_names(names2(x), repair_hint, .name_repair = .name_repair, 
 .     quiet = quiet, call = call)
5. subclass_name_repair_errors(name = name, details = details, repair_hint = repair_hint, 
 .     vec_as_names(name, repair = .name_repair, quiet = quiet || 
 .         !is_character(.name_repair)), call = call)
6. withCallingHandlers(expr, vctrs_error_names_cannot_be_empty = function(cnd) {
 .     abort_column_names_cannot_be_empty(detect_empty_names(name), 
 .         details = details, parent = cnd, repair_hint = repair_hint, 
 .         call = call)
 . }, vctrs_error_names_cannot_be_dot_dot = function(cnd) {
 .     abort_column_names_cannot_be_dot_dot(detect_dot_dot(name), 
 .         parent = cnd, repair_hint = repair_hint, call = call)
 . }, vctrs_error_names_must_be_unique = function(cnd) {
 .     abort_column_names_must_be_unique(detect_duplicates(name), 
 .         parent = cnd, repair_hint = repair_hint, call = call)
 . })
7. vec_as_names(name, repair = .name_repair, quiet = quiet || !is_character(.name_repair))
8. (function () 
 . validate_unique(names = names, arg = arg, call = call))()
9. validate_unique(names = names, arg = arg, call = call)
10. stop_names_must_be_unique(names, arg, call = call)
11. stop_names(class = "vctrs_error_names_must_be_unique", arg = arg, 
  .     names = names, call = call)
12. stop_vctrs(class = c(class, "vctrs_error_names"), ..., call = call)
13. abort(message, class = c(class, "vctrs_error"), ..., call = call)
14. signal_abort(cnd, .file)
15. signalCondition(cnd)
16. (function (cnd) 
  . {
  .     abort_column_names_must_be_unique(detect_duplicates(name), 
  .         parent = cnd, repair_hint = repair_hint, call = call)
  . })(structure(list(message = "", trace = structure(list(call = list(
  .     IRkernel::main(), kernel$run(), handle_shell(), executor$execute(msg), 
  .     tryCatch(evaluate(request$content$code, envir = .GlobalEnv, 
  .         output_handler = oh, stop_on_error = 1L), interrupt = function(cond) {
  .         log_debug("Interrupt during execution")
  .         interrupted <<- TRUE
  .     }, error = .self$handle_error), tryCatchList(expr, classes, 
  .         parentenv, handlers), tryCatchOne(tryCatchList(expr, 
  .         names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, 
  .         handlers[[nh]]), doTryCatch(return(expr), name, parentenv, 
  .         handler), tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), 
  .     tryCatchOne(expr, names, parentenv, handlers[[1L]]), doTryCatch(return(expr), 
  .         name, parentenv, handler), evaluate(request$content$code, 
  .         envir = .GlobalEnv, output_handler = oh, stop_on_error = 1L), 
  .     evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, 
  .         debug = debug, last = i == length(out), use_try = stop_on_error != 
  .             2L, keep_warning = keep_warning, keep_message = keep_message, 
  .         log_echo = log_echo, log_warning = log_warning, output_handler = output_handler, 
  .         include_timing = include_timing), timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
  .         message = mHandler))), handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
  .         message = mHandler)), try(f, silent = TRUE), tryCatch(expr, 
  .         error = function(e) {
  .             call <- conditionCall(e)
  .             if (!is.null(call)) {
  .                 if (identical(call[[1L]], quote(doTryCatch))) 
  .                   call <- sys.call(-4L)
  .                 dcall <- deparse(call, nlines = 1L)
  .                 prefix <- paste("Error in", dcall, ": ")
  .                 LONG <- 75L
  .                 sm <- strsplit(conditionMessage(e), "\n")[[1L]]
  .                 w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], 
  .                   type = "w")
  .                 if (is.na(w)) 
  .                   w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], 
  .                     type = "b")
  .                 if (w > LONG) 
  .                   prefix <- paste0(prefix, "\n  ")
  .             }
  .             else prefix <- "Error : "
  .             msg <- paste0(prefix, conditionMessage(e), "\n")
  .             .Internal(seterrmessage(msg[1L]))
  .             if (!silent && isTRUE(getOption("show.error.messages"))) {
  .                 cat(msg, file = outFile)
  .                 .Internal(printDeferredWarnings())
  .             }
  .             invisible(structure(msg, class = "try-error", condition = e))
  .         }), tryCatchList(expr, classes, parentenv, handlers), 
  .     tryCatchOne(expr, names, parentenv, handlers[[1L]]), doTryCatch(return(expr), 
  .         name, parentenv, handler), withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
  .         message = mHandler), withVisible(eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers)), eval_with_user_handlers(expr, 
  .         envir, enclos, user_handlers), eval(expr, envir, enclos), 
  .     eval(expr, envir, enclos), tibble(x = 1, x = 2), tibble_quos(xs, 
  .         .rows, .name_repair), set_repaired_names(output, repair_hint = TRUE, 
  .         .name_repair = .name_repair, call = call), repaired_names(names2(x), 
  .         repair_hint, .name_repair = .name_repair, quiet = quiet, 
  .         call = call), subclass_name_repair_errors(name = name, 
  .         details = details, repair_hint = repair_hint, vec_as_names(name, 
  .             repair = .name_repair, quiet = quiet || !is_character(.name_repair)), 
  .         call = call), withCallingHandlers(expr, vctrs_error_names_cannot_be_empty = function(cnd) {
  .         abort_column_names_cannot_be_empty(detect_empty_names(name), 
  .             details = details, parent = cnd, repair_hint = repair_hint, 
  .             call = call)
  .     }, vctrs_error_names_cannot_be_dot_dot = function(cnd) {
  .         abort_column_names_cannot_be_dot_dot(detect_dot_dot(name), 
  .             parent = cnd, repair_hint = repair_hint, call = call)
  .     }, vctrs_error_names_must_be_unique = function(cnd) {
  .         abort_column_names_must_be_unique(detect_duplicates(name), 
  .             parent = cnd, repair_hint = repair_hint, call = call)
  .     }), vec_as_names(name, repair = .name_repair, quiet = quiet || 
  .         !is_character(.name_repair)), `<fn>`(), validate_unique(names = names, 
  .         arg = arg, call = call), stop_names_must_be_unique(names, 
  .         arg, call = call), stop_names(class = "vctrs_error_names_must_be_unique", 
  .         arg = arg, names = names, call = call), stop_vctrs(class = c(class, 
  .         "vctrs_error_names"), ..., call = call), abort(message, 
  .         class = c(class, "vctrs_error"), ..., call = call)), 
  .     parent = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 6L, 9L, 10L, 4L, 
  .     12L, 13L, 13L, 15L, 16L, 17L, 18L, 19L, 13L, 13L, 13L, 23L, 
  .     24L, 0L, 26L, 27L, 28L, 29L, 30L, 29L, 32L, 33L, 34L, 35L, 
  .     36L, 37L), visible = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, 
  .     TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, 
  .     TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, 
  .     TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  .     FALSE, FALSE, FALSE), namespace = c("IRkernel", NA, "IRkernel", 
  .     NA, "base", "base", "base", "base", "base", "base", "base", 
  .     "evaluate", "evaluate", "evaluate", "evaluate", "base", "base", 
  .     "base", "base", "base", "base", "base", "evaluate", "base", 
  .     "base", "tibble", "tibble", "tibble", "tibble", "tibble", 
  .     "base", "vctrs", "vctrs", "vctrs", "vctrs", "vctrs", "vctrs", 
  .     "rlang"), scope = c("::", NA, "local", NA, "::", "local", 
  .     "local", "local", "local", "local", "local", "::", ":::", 
  .     "local", "local", "::", "::", "local", "local", "local", 
  .     "::", "::", ":::", "::", "::", "::", ":::", ":::", ":::", 
  .     ":::", "::", "::", "local", ":::", ":::", ":::", ":::", "::"
  .     ), error_frame = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  .     FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  .     FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
  .     FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, 
  .     FALSE, FALSE, FALSE, FALSE, FALSE)), row.names = c(NA, -38L
  . ), version = 2L, class = c("rlang_trace", "rlib_trace", "tbl", 
  . "data.frame")), parent = NULL, arg = NULL, names = c("x", "x"
  . ), rlang = list(inherit = TRUE), call = repaired_names(names2(x), 
  .     repair_hint, .name_repair = .name_repair, quiet = quiet, 
  .     call = call)), class = c("vctrs_error_names_must_be_unique", 
  . "vctrs_error_names", "vctrs_error", "rlang_error", "error", "condition"
  . )))
17. abort_column_names_must_be_unique(detect_duplicates(name), parent = cnd, 
  .     repair_hint = repair_hint, call = call)
18. tibble_abort(invalid_df("must not be duplicated", names, use_repair(repair_hint), 
  .     message = "Column name(s)"), names = names, parent = parent, 
  .     call = call)
19. abort(x, class, ..., call = call, parent = parent, use_cli_format = TRUE)
20. signal_abort(cnd, .file)

tibble(x = 1, x = 2, .name_repair = "minimal")
tibble(x = 1, x = 2, .name_repair = "unique")
tibble(x = 1, x = 2, .name_repair = "universal")

A tibble: 1 × 2
x	x
<dbl>	<dbl>
1	2

New names:
• `x` -> `x...1`
• `x` -> `x...2`

A tibble: 1 × 2
x...1	x...2
<dbl>	<dbl>
1	2

New names:
• `x` -> `x...1`
• `x` -> `x...2`

A tibble: 1 × 2
x...1	x...2
<dbl>	<dbl>
1	2

tibble(x = 1, x = 2, .name_repair = make.unique) # 指定修复函数
tibble(x = 1, x = 2, .name_repair = ~make.unique(.x, sep = "_"))
tibble(x = 1, x = 2, .name_repair = ~make.names(., unique = TRUE))

A tibble: 1 × 2
x	x.1
<dbl>	<dbl>
1	2

A tibble: 1 × 2
x	x_1
<dbl>	<dbl>
1	2

A tibble: 1 × 2
x	x.1
<dbl>	<dbl>
1	2

注意make.unique(names, sep = ".")和make.names(names, unique = FALSE, allow_ = TRUE) 是基础包的函数

`List-columns`(列表列)#

tibble 本质上是向量构成的列表

大多情况下，我们接触到的向量是原子型向量(atomic vectors)，所谓原子型向量就是向量元素为单个值，比如 “a” 或者 1

tibble还有可以允许某一列为列表(list)，那么列表构成的列，称之为列表列（list columns）

这样一来，列表列非常灵活，因为列表元素可以是原子型向量、列表、矩阵或者小的tibble

`nested tibble`#

tibble的列表列装载数据的能力很强大，也很灵活。

如何创建和操控有列表列的tibble。

1 `creating`#

假定我们这里有一个tibble，我们有三种方法可以创建列表列

nest()
summarise() and list()
mutate() and map()

tidyr::nest()创建

使用tidyr::nest(data = c())函数，创建有列表列的tibble, data指定那几列合成列表列data。

除了x列外的其他列就可用nest(data = !x)

# tidyr::nest()
library(tidyverse)
library(palmerpenguins)
df <- penguins %>% 
  drop_na() %>% 
  select(species, bill_length_mm, bill_depth_mm, body_mass_g)
df %>% head()

tb <- df %>% 
  tidyr::nest(data = c(bill_length_mm, bill_depth_mm, body_mass_g))
tb %>% head()

A tibble: 6 × 4
species	bill_length_mm	bill_depth_mm	body_mass_g
<fct>	<dbl>	<dbl>	<int>
Adelie	39.1	18.7	3750
Adelie	39.5	17.4	3800
Adelie	40.3	18.0	3250
Adelie	36.7	19.3	3450
Adelie	39.3	20.6	3650
Adelie	38.9	17.8	3625

A tibble: 3 × 2
species	data
<fct>	<list>
Adelie	39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0
Gentoo	46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0
Chinstrap	46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0

nest() 为每种species创建了一个小的tibble，每个小的tibble对应一个species

tb$data[[1]] %>% head()
tb$data %>% typeof()
tb$data %>% class()

A tibble: 6 × 3
bill_length_mm	bill_depth_mm	body_mass_g
<dbl>	<dbl>	<int>
39.1	18.7	3750
39.5	17.4	3800
40.3	18.0	3250
36.7	19.3	3450
39.3	20.6	3650
38.9	17.8	3625

'list'

# 除了species列之外的其他列组合成list_columns
df %>% 
  nest(data = !species)

A tibble: 3 × 2
species	data
<fct>	<list>
Adelie	39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0
Gentoo	46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0
Chinstrap	46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0

# 同时创建多个列表列
df %>% 
  nest(data1 = c(bill_length_mm, bill_depth_mm), data2 = body_mass_g)

A tibble: 3 × 3
species	data1	data2
<fct>	<list>	<list>
Adelie	39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5	3750, 3800, 3250, 3450, 3650, 3625, 4675, 3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 3800, 3950, 3800, 3800, 3550, 3200, 3150, 3950, 3250, 3900, 3300, 3900, 3325, 4150, 3950, 3550, 3300, 4650, 3150, 3900, 3100, 4400, 3000, 4600, 3425, 3450, 4150, 3500, 4300, 3450, 4050, 2900, 3700, 3550, 3800, 2850, 3750, 3150, 4400, 3600, 4050, 2850, 3950, 3350, 4100, 3050, 4450, 3600, 3900, 3550, 4150, 3700, 4250, 3700, 3900, 3550, 4000, 3200, 4700, 3800, 4200, 3350, 3550, 3800, 3500, 3950, 3600, 3550, 4300, 3400, 4450, 3300, 4300, 3700, 4350, 2900, 4100, 3725, 4725, 3075, 4250, 2925, 3550, 3750, 3900, 3175, 4775, 3825, 4600, 3200, 4275, 3900, 4075, 2900, 3775, 3350, 3325, 3150, 3500, 3450, 3875, 3050, 4000, 3275, 4300, 3050, 4000, 3325, 3500, 3500, 4475, 3425, 3900, 3175, 3975, 3400, 4250, 3400, 3475, 3050, 3725, 3000, 3650, 4250, 3475, 3450, 3750, 3700, 4000
Gentoo	46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1	4500, 5700, 4450, 5700, 5400, 4550, 4800, 5200, 4400, 5150, 4650, 5550, 4650, 5850, 4200, 5850, 4150, 6300, 4800, 5350, 5700, 5000, 4400, 5050, 5000, 5100, 5650, 4600, 5550, 5250, 4700, 5050, 6050, 5150, 5400, 4950, 5250, 4350, 5350, 3950, 5700, 4300, 4750, 5550, 4900, 4200, 5400, 5100, 5300, 4850, 5300, 4400, 5000, 4900, 5050, 4300, 5000, 4450, 5550, 4200, 5300, 4400, 5650, 4700, 5700, 5800, 4700, 5550, 4750, 5000, 5100, 5200, 4700, 5800, 4600, 6000, 4750, 5950, 4625, 5450, 4725, 5350, 4750, 5600, 4600, 5300, 4875, 5550, 4950, 5400, 4750, 5650, 4850, 5200, 4925, 4875, 4625, 5250, 4850, 5600, 4975, 5500, 5500, 4700, 5500, 4575, 5500, 5000, 5950, 4650, 5500, 4375, 5850, 6000, 4925, 4850, 5750, 5200, 5400
Chinstrap	46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7	3500, 3900, 3650, 3525, 3725, 3950, 3250, 3750, 4150, 3700, 3800, 3775, 3700, 4050, 3575, 4050, 3300, 3700, 3450, 4400, 3600, 3400, 2900, 3800, 3300, 4150, 3400, 3800, 3700, 4550, 3200, 4300, 3350, 4100, 3600, 3900, 3850, 4800, 2700, 4500, 3950, 3650, 3550, 3500, 3675, 4450, 3400, 4300, 3250, 3675, 3325, 3950, 3600, 4050, 3350, 3450, 3250, 4050, 3800, 3525, 3950, 3650, 3650, 4000, 3400, 3775, 4100, 3775

1 creating#

`tidyr::summarise(list())`创建#

group_by() 和 summarise()组合可以将向量分组后分别压缩成单个值，事实上，summarise()还可以创建列表列。

df_collpase <- df %>% 
  group_by(species) %>% 
  summarise(data = list(bill_length_mm))
df_collpase

A tibble: 3 × 2
species	data
<fct>	<list>
Adelie	39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5
Chinstrap	46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2
Gentoo	46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9

data就是构建的列表列，它的每个元素都是一个向量，对应一个species。这种方法和nest()方法很相似，不同在于，summarise() + list() 构建的列表列其元素是原子型向量，而nest()构建的是tibble.

df_collpase$data[[1]] %>% typeof()

'double'

summarise() + list()的方法还可以在创建列表列之前，对数据简单处理

# 排序
df %>% 
  group_by(species) %>% 
  summarise(data = list(sort(bill_length_mm)))

# 筛选
df %>% 
  group_by(species) %>% 
  summarise(data = list(bill_length_mm[bill_length_mm > 45]))

A tibble: 3 × 2
species	data
<fct>	<list>
Adelie	32.1, 33.1, 33.5, 34.0, 34.4, 34.5, 34.6, 34.6, 35.0, 35.0, 35.1, 35.2, 35.3, 35.5, 35.5, 35.6, 35.7, 35.7, 35.7, 35.9, 35.9, 36.0, 36.0, 36.0, 36.0, 36.2, 36.2, 36.2, 36.3, 36.4, 36.4, 36.5, 36.5, 36.6, 36.6, 36.7, 36.7, 36.8, 36.9, 37.0, 37.0, 37.2, 37.2, 37.3, 37.3, 37.3, 37.5, 37.6, 37.6, 37.6, 37.7, 37.7, 37.7, 37.8, 37.8, 37.8, 37.9, 37.9, 38.1, 38.1, 38.1, 38.1, 38.2, 38.2, 38.3, 38.5, 38.6, 38.6, 38.6, 38.7, 38.8, 38.8, 38.8, 38.9, 38.9, 39.0, 39.0, 39.0, 39.1, 39.2, 39.2, 39.2, 39.3, 39.5, 39.5, 39.5, 39.6, 39.6, 39.6, 39.6, 39.6, 39.7, 39.7, 39.7, 39.7, 39.8, 40.1, 40.2, 40.2, 40.2, 40.3, 40.3, 40.5, 40.5, 40.6, 40.6, 40.6, 40.6, 40.7, 40.8, 40.8, 40.9, 40.9, 41.0, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.1, 41.3, 41.3, 41.4, 41.4, 41.5, 41.5, 41.6, 41.8, 42.0, 42.1, 42.2, 42.2, 42.3, 42.5, 42.7, 42.8, 42.9, 43.1, 43.2, 43.2, 44.1, 44.1, 45.6, 45.8, 46.0
Chinstrap	40.9, 42.4, 42.5, 42.5, 43.2, 43.5, 45.2, 45.2, 45.4, 45.5, 45.6, 45.7, 45.7, 45.9, 46.0, 46.1, 46.2, 46.4, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47.0, 47.5, 47.6, 48.1, 48.5, 49.0, 49.0, 49.2, 49.3, 49.5, 49.6, 49.7, 49.8, 50.0, 50.1, 50.2, 50.2, 50.3, 50.5, 50.5, 50.6, 50.7, 50.8, 50.8, 50.9, 50.9, 51.0, 51.3, 51.3, 51.3, 51.4, 51.5, 51.7, 51.9, 52.0, 52.0, 52.0, 52.2, 52.7, 52.8, 53.5, 54.2, 55.8, 58.0
Gentoo	40.9, 41.7, 42.0, 42.6, 42.7, 42.8, 42.9, 43.2, 43.3, 43.3, 43.4, 43.5, 43.5, 43.6, 43.8, 44.0, 44.4, 44.5, 44.9, 44.9, 45.0, 45.1, 45.1, 45.1, 45.2, 45.2, 45.2, 45.2, 45.3, 45.3, 45.4, 45.5, 45.5, 45.5, 45.5, 45.7, 45.8, 45.8, 46.1, 46.1, 46.2, 46.2, 46.2, 46.3, 46.4, 46.4, 46.5, 46.5, 46.5, 46.5, 46.6, 46.7, 46.8, 46.8, 46.8, 46.9, 47.2, 47.2, 47.3, 47.4, 47.5, 47.5, 47.5, 47.6, 47.7, 47.8, 48.1, 48.2, 48.2, 48.4, 48.4, 48.4, 48.5, 48.5, 48.6, 48.7, 48.7, 48.7, 48.8, 49.0, 49.1, 49.1, 49.1, 49.2, 49.3, 49.4, 49.5, 49.5, 49.6, 49.6, 49.8, 49.8, 49.9, 50.0, 50.0, 50.0, 50.0, 50.1, 50.2, 50.4, 50.4, 50.5, 50.5, 50.5, 50.7, 50.8, 50.8, 51.1, 51.1, 51.3, 51.5, 52.1, 52.2, 52.5, 53.4, 54.3, 55.1, 55.9, 59.6

A tibble: 3 × 2
species	data
<fct>	<list>
Adelie	46.0, 45.8, 45.6
Chinstrap	46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 48.5, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 54.2, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 49.6, 50.8, 50.2
Gentoo	46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 46.8, 49.0, 45.5, 48.4, 45.8, 49.3, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 46.1, 47.8, 48.2, 50.0, 47.3, 45.1, 59.6, 49.1, 48.4, 48.7, 49.6, 45.3, 49.6, 50.5, 45.5, 50.5, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.5, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 50.8, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 53.4, 48.1, 50.5, 49.8, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9

1 creating#

`dplyr::mutate()`创建#

第三种方法是用rowwise() + mutate()，比如，下面为每个岛屿(island) 创建一个与该岛企鹅数量等长的随机数向量，简单点说，这个岛屿上企鹅有多少只，那么随机数的个数就有多少个。
rowwise()对数据后续的操作按行进行

penguins %>% 
  drop_na() %>% 
  group_by(species) %>% 
  summarise(
    n_num = n()
  ) %>% 
  
  rowwise() %>% 
  mutate(random = list(rnorm(n = n_num))) %>% 
  ungroup()

A tibble: 3 × 3
species	n_num	random
<fct>	<int>	<list>
Adelie	146	1.167295e+00, -4.737190e-01, 1.701407e-01, -3.335451e-01, -9.898292e-01, -6.296705e-01, -1.124165e+00, -1.193101e+00, 7.645301e-01, 1.450047e+00, 8.943789e-01, 1.567609e-01, -8.405838e-01, 1.351153e+00, 1.476331e+00, 9.725533e-01, 1.186655e+00, -1.348885e+00, 1.329209e+00, 2.538072e-02, -1.028830e+00, 3.522572e-02, 2.405497e-01, -6.465222e-02, -2.219975e-01, 7.636225e-01, -1.237271e+00, 5.960882e-01, 5.003514e-01, 3.162578e-01, 5.795788e-01, -2.764559e-01, 1.524357e+00, -2.892280e-02, -1.579660e-01, 2.379252e+00, 1.394055e+00, -1.959195e-01, -8.713394e-01, 8.773831e-01, 3.543745e-01, 4.692338e-01, 4.224226e-01, -6.829615e-01, 1.146301e+00, -9.541174e-01, -7.835406e-01, 1.586814e+00, -1.968128e-01, -2.270889e-01, 7.163727e-01, 8.687031e-03, 8.596198e-05, -1.700675e+00, -7.175757e-02, 4.819658e-01, -1.952466e+00, 8.896643e-01, -9.224212e-01, 3.684734e-01, 9.594264e-01, -3.521724e-02, -4.861386e-01, 8.402146e-01, -1.317366e+00, -6.080339e-03, -1.198203e+00, -2.845206e-01, -4.009939e-01, -1.910883e+00, -1.469575e+00, -2.453137e-01, 1.251781e-01, 7.573974e-01, -1.255012e+00, 5.886890e-01, -5.188868e-01, 1.658795e-01, -2.010551e-01, -3.028571e-01, -1.239115e+00, 2.794436e-01, -1.265087e+00, -1.223223e+00, 1.446148e+00, -1.437441e-01, 4.305247e-01, 2.404749e-01, -8.181399e-01, 2.565301e-01, 1.746820e-01, 1.125191e+00, 7.985691e-01, 1.733713e+00, -6.221736e-01, -2.508314e-01, 2.580685e+00, -1.834283e-01, -1.760125e-01, -5.151157e-02, 2.008103e-01, -1.706216e+00, -1.522744e+00, 9.237093e-01, 6.510890e-01, 1.445042e+00, 1.378860e+00, 1.020683e+00, -3.014718e-01, 1.409915e-02, 2.537571e-01, -1.138473e+00, 1.497061e+00, -7.595592e-01, 4.881166e-01, -6.423024e-01, 3.949879e-01, -1.037229e+00, -1.972556e-01, 1.126628e-01, 5.994793e-01, 1.133124e+00, -1.120773e+00, -2.198435e+00, 3.384433e-01, -1.960774e-01, 1.871556e+00, 1.794224e+00, 4.365376e-01, -1.066723e+00, 5.293482e-01, -7.347930e-01, -3.003514e-01, 1.105051e+00, 8.212121e-01, 1.626330e-01, 1.482554e+00, -5.118503e-01, 6.861231e-01, -3.918395e-01, 4.162947e-01, 2.863751e-01, -1.116953e+00, 6.488546e-01, 2.596957e+00, 9.095422e-01
Chinstrap	68	0.249621201, -0.142766166, -1.757789978, 0.075659190, -0.542204636, -0.518322686, 0.550188750, -1.480486487, 0.517286946, -0.478879994, 0.684933500, -0.833897305, -0.045534657, -2.264620283, 1.626703542, 3.186839338, 1.657139387, -2.257361800, 1.084688341, -0.871035848, 0.744813929, -1.057977728, -0.283933183, -0.361579325, 1.609372393, -0.937747959, 0.927502854, 0.342427027, -0.252874063, 0.646968066, 0.514054071, -0.007504106, -0.261007696, -0.402997129, 1.563733714, -1.372970252, -0.873958043, -1.579596945, 0.793006133, 1.047765639, 0.672742015, 0.893119151, -1.411646446, 0.949228949, -0.851395710, -1.700535007, -0.913513746, -0.075950418, 0.350204001, -0.199606382, 0.162862598, -0.398026660, 1.641816115, -0.427212654, -0.603435322, 0.077376810, -0.929106303, 0.159376032, -0.744075421, 1.653830140, -1.043284588, -0.027990136, 0.648602174, -0.730073813, -1.742943053, 0.319850717, -0.026872356, 0.078055293
Gentoo	119	-0.92061868, -0.34422337, -0.43974025, -0.63369439, -0.69049605, -0.17852675, -2.55892072, -0.26470162, -0.01946695, -0.48271763, 0.72352453, -1.07961965, -0.91748793, 1.29190496, -2.41062712, 0.71692822, 0.42423898, -1.39348027, -0.36407719, -2.52327668, -1.24569768, 0.89102824, -0.42592174, 0.13840190, 0.08962038, 0.24565338, 0.13801438, -1.01583155, -1.12003831, 0.22890692, -1.46114934, -0.17640229, -0.89778500, 0.70186528, 1.06463294, 1.69557583, 1.63852787, 0.59937431, 0.63856050, -0.17927942, 0.32968269, -1.02889519, -0.32432437, -0.11862299, 0.78757915, -0.59803216, -0.34551199, 0.35241593, -0.83002695, 0.20047851, -0.40422229, -1.07998634, -2.20280917, 2.33545120, -1.13139797, -0.25935082, 0.19788404, 0.48247387, 0.81284930, -0.38799659, 0.99345484, 0.43704157, -0.96237573, 1.56689496, -1.04441203, 1.03648421, 0.04951044, 0.25045102, 1.31696451, 1.34275301, -1.96678414, 1.17927099, 0.63157344, 0.04906126, 1.76610143, -0.98288349, 0.34501498, -0.07014398, -1.33683859, -0.33477371, 1.42787656, -0.03864391, -1.42388130, 0.73983620, -1.23099857, -0.80668893, -1.55632721, -0.68268313, 1.24072957, -1.64219386, 0.03097835, 1.29566617, 1.58465137, -0.41897228, -0.18271749, 1.01965393, -1.43888829, 0.38167321, -0.69379819, 0.59007243, -2.12267158, 1.52480976, -0.92013459, 0.96568091, 0.68673866, 0.43035408, -0.44958923, -1.62758342, 0.38411198, 0.40862634, 0.53522276, -1.49477309, -0.02041033, 0.41120502, 1.00729973, 0.22120862, -0.16425353, -1.28981205, -0.79913234

2 Unnesting#

用unnest(cols = )函数可以把列表列转换成常规列的形式,也就是还原成正常样式

tb

A tibble: 3 × 2
species	data
<fct>	<list>
Adelie	39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0
Gentoo	46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0
Chinstrap	46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0

tb %>% 
  unnest(cols = data) %>% 
  head()

A tibble: 6 × 4
species	bill_length_mm	bill_depth_mm	body_mass_g
<fct>	<dbl>	<dbl>	<int>
Adelie	39.1	18.7	3750
Adelie	39.5	17.4	3800
Adelie	40.3	18.0	3250
Adelie	36.7	19.3	3450
Adelie	39.3	20.6	3650
Adelie	38.9	17.8	3625

Manipulating#

操控列表列是一件有趣的事情，我们常常会借助于行方向的操作(rowwise)。比如找出每个岛屿企鹅的数量，我们需要对data列表列的元素依次迭代，

tb %>% 
  rowwise() %>% 
  mutate(num_species = nrow(data))

A rowwise_df: 3 × 3
species	data	num_species
<fct>	<list>	<int>
Adelie	39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0	146
Gentoo	46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0	119
Chinstrap	46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0	68

# 求每组下企鹅嘴峰长度与嘴峰厚度的相关系数
tb %>% 
  rowwise() %>% 
  mutate(corr_coef = cor(data$bill_length_mm, data$bill_depth_mm))

A rowwise_df: 3 × 3
species	data	corr_coef
<fct>	<list>	<dbl>
Adelie	39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46.0, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36.0, 44.1, 37.0, 39.6, 41.1, 36.0, 42.3, 39.6, 40.1, 35.0, 42.0, 34.5, 41.4, 39.0, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34.0, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35.0, 41.0, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39.0, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37.0, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39.0, 39.2, 36.6, 36.0, 37.8, 36.0, 41.5, 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2, 21.1, 17.8, 19.0, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17.0, 21.1, 20.0, 18.5, 19.3, 19.1, 18.0, 18.4, 18.5, 19.7, 16.9, 18.8, 19.0, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17.0, 18.2, 17.1, 18.0, 16.2, 19.1, 16.6, 19.4, 19.0, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18.0, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20.0, 16.0, 20.0, 18.6, 18.9, 17.2, 20.0, 17.0, 19.0, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17.0, 20.5, 17.0, 18.6, 17.2, 19.8, 17.0, 18.5, 15.9, 19.0, 17.6, 18.3, 17.1, 18.0, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17.0, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 3750.0, 3800.0, 3250.0, 3450.0, 3650.0, 3625.0, 4675.0, 3200.0, 3800.0, 4400.0, 3700.0, 3450.0, 4500.0, 3325.0, 4200.0, 3400.0, 3600.0, 3800.0, 3950.0, 3800.0, 3800.0, 3550.0, 3200.0, 3150.0, 3950.0, 3250.0, 3900.0, 3300.0, 3900.0, 3325.0, 4150.0, 3950.0, 3550.0, 3300.0, 4650.0, 3150.0, 3900.0, 3100.0, 4400.0, 3000.0, 4600.0, 3425.0, 3450.0, 4150.0, 3500.0, 4300.0, 3450.0, 4050.0, 2900.0, 3700.0, 3550.0, 3800.0, 2850.0, 3750.0, 3150.0, 4400.0, 3600.0, 4050.0, 2850.0, 3950.0, 3350.0, 4100.0, 3050.0, 4450.0, 3600.0, 3900.0, 3550.0, 4150.0, 3700.0, 4250.0, 3700.0, 3900.0, 3550.0, 4000.0, 3200.0, 4700.0, 3800.0, 4200.0, 3350.0, 3550.0, 3800.0, 3500.0, 3950.0, 3600.0, 3550.0, 4300.0, 3400.0, 4450.0, 3300.0, 4300.0, 3700.0, 4350.0, 2900.0, 4100.0, 3725.0, 4725.0, 3075.0, 4250.0, 2925.0, 3550.0, 3750.0, 3900.0, 3175.0, 4775.0, 3825.0, 4600.0, 3200.0, 4275.0, 3900.0, 4075.0, 2900.0, 3775.0, 3350.0, 3325.0, 3150.0, 3500.0, 3450.0, 3875.0, 3050.0, 4000.0, 3275.0, 4300.0, 3050.0, 4000.0, 3325.0, 3500.0, 3500.0, 4475.0, 3425.0, 3900.0, 3175.0, 3975.0, 3400.0, 4250.0, 3400.0, 3475.0, 3050.0, 3725.0, 3000.0, 3650.0, 4250.0, 3475.0, 3450.0, 3750.0, 3700.0, 4000.0	0.3858132
Gentoo	46.1, 50.0, 48.7, 50.0, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49.0, 45.5, 48.4, 45.8, 49.3, 42.0, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 47.8, 48.2, 50.0, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44.0, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45.0, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50.0, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 48.8, 47.2, 46.8, 50.4, 45.2, 49.9, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 15.0, 14.3, 15.3, 15.3, 14.2, 14.5, 17.0, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16.0, 13.7, 15.0, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15.0, 14.4, 15.4, 13.9, 15.0, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 16.2, 14.2, 15.0, 15.0, 15.6, 15.6, 14.8, 15.0, 16.0, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14.0, 17.0, 15.0, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15.0, 17.0, 15.5, 15.0, 16.1, 14.7, 15.8, 14.0, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16.0, 16.2, 13.7, 14.3, 15.7, 14.8, 16.1, 4500.0, 5700.0, 4450.0, 5700.0, 5400.0, 4550.0, 4800.0, 5200.0, 4400.0, 5150.0, 4650.0, 5550.0, 4650.0, 5850.0, 4200.0, 5850.0, 4150.0, 6300.0, 4800.0, 5350.0, 5700.0, 5000.0, 4400.0, 5050.0, 5000.0, 5100.0, 5650.0, 4600.0, 5550.0, 5250.0, 4700.0, 5050.0, 6050.0, 5150.0, 5400.0, 4950.0, 5250.0, 4350.0, 5350.0, 3950.0, 5700.0, 4300.0, 4750.0, 5550.0, 4900.0, 4200.0, 5400.0, 5100.0, 5300.0, 4850.0, 5300.0, 4400.0, 5000.0, 4900.0, 5050.0, 4300.0, 5000.0, 4450.0, 5550.0, 4200.0, 5300.0, 4400.0, 5650.0, 4700.0, 5700.0, 5800.0, 4700.0, 5550.0, 4750.0, 5000.0, 5100.0, 5200.0, 4700.0, 5800.0, 4600.0, 6000.0, 4750.0, 5950.0, 4625.0, 5450.0, 4725.0, 5350.0, 4750.0, 5600.0, 4600.0, 5300.0, 4875.0, 5550.0, 4950.0, 5400.0, 4750.0, 5650.0, 4850.0, 5200.0, 4925.0, 4875.0, 4625.0, 5250.0, 4850.0, 5600.0, 4975.0, 5500.0, 5500.0, 4700.0, 5500.0, 4575.0, 5500.0, 5000.0, 5950.0, 4650.0, 5500.0, 4375.0, 5850.0, 6000.0, 4925.0, 4850.0, 5750.0, 5200.0, 5400.0	0.6540233
Chinstrap	46.5, 50.0, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46.0, 51.3, 46.6, 51.7, 47.0, 52.0, 45.9, 50.5, 50.3, 58.0, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52.0, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51.0, 49.7, 47.5, 47.6, 52.0, 46.9, 53.5, 49.0, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49.0, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20.0, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19.0, 18.4, 19.0, 17.8, 20.0, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17.0, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19.0, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17.0, 19.8, 18.1, 18.2, 19.0, 18.7, 3500.0, 3900.0, 3650.0, 3525.0, 3725.0, 3950.0, 3250.0, 3750.0, 4150.0, 3700.0, 3800.0, 3775.0, 3700.0, 4050.0, 3575.0, 4050.0, 3300.0, 3700.0, 3450.0, 4400.0, 3600.0, 3400.0, 2900.0, 3800.0, 3300.0, 4150.0, 3400.0, 3800.0, 3700.0, 4550.0, 3200.0, 4300.0, 3350.0, 4100.0, 3600.0, 3900.0, 3850.0, 4800.0, 2700.0, 4500.0, 3950.0, 3650.0, 3550.0, 3500.0, 3675.0, 4450.0, 3400.0, 4300.0, 3250.0, 3675.0, 3325.0, 3950.0, 3600.0, 4050.0, 3350.0, 3450.0, 3250.0, 4050.0, 3800.0, 3525.0, 3950.0, 3650.0, 3650.0, 4000.0, 3400.0, 3775.0, 4100.0, 3775.0	0.6535362

函数式编程1——`purrr`#

R常用的数据结构是向量、矩阵、列表和数据框

他们构造起来，很多相似性。

        list(a = 1, b = "a")   # list
           c(a = 1, b = 2)     # named vector
  data.frame(a = 1, b = 2)     # data frame
      tibble(a = 1, b = 2)     # tibble

$a: 1
$b: 'a'

a: 1
b: 2

A data.frame: 1 × 2
a	b
<dbl>	<dbl>
1	2

A tibble: 1 × 2
a	b
<dbl>	<dbl>
1	2

向量化运算#

a <- c(2, 4, 3, 1, 5, 7)

用for()循环，让向量的每个元素乘以2

for (i in 1:length(a)){
    print(a[i] * 2)
}

[1] 4
[1] 8
[1] 6
[1] 2
[1] 10
[1] 14

事实上，R语言是支持向量化（将运算符或者函数作用在向量的每一个元素上），可以用向量化代替循环

a * 2

4
8
6
2
10
14

再比如，找出向量a中元素大于2的所有值

for (i in 1:length(a)){
    if (a[i] > 2)
        {print(a[i])}
}

[1] 4
[1] 3
[1] 5
[1] 7

用向量化的运算，可以轻松实现

a[a > 2]

4
3
5
7

列表#

a_list <- list(
  num = c(8,9),
  log = TRUE,
  cha = c("a", "b", "c")
)
a_list

$num

8
9

$log

TRUE

$cha

'a'
'b'
'c'

要想访问某个元素，可以这样

a_list["log"]

$log = TRUE

注意返回结果，第一行是$log，说明返回的结果仍然是列表, 相比a_list来说，a_list["log"]是只包含一个元素的列表。

将log元素里面的向量提取出来，就得用两个[[或者$

a_list[["log"]]
a_list$log

TRUE

在tidyverse里，还可以用

a_list %>% pluck(1)
a_list %>% pluck("num")

8
9

8
9

列表 vs 向量#

v <- c(-2, -1, 0, 1, 2)
abs(v)

2
1
0
1
2

如果是列表形式，abs函数应用到列表中就会报错

lst <- list(-2, -1, 0, 1, 2)
abs(lst)

Error in abs(lst): 数学函数中用了非数值参数
Traceback:

用在向量的函数用在list上，往往行不通。

exams <- list(
  student1 = round(runif(10, 50, 100)),
  student2 = round(runif(10, 50, 100)),
  student3 = round(runif(10, 50, 100)),
  student4 = round(runif(10, 50, 100)),
  student5 = round(runif(10, 50, 100))
)
exams

$student1

89
70
68
98
74
81
99
88
62
67

$student2

75
57
76
94
73
76
73
72
84
66

$student3

54
65
82
85
96
87
86
77
98
57

$student4

65
70
71
90
81
86
55
90
76
86

$student5

57
55
100
65
72
81
74
62
62
51

对列表执行向量函数的运算会很不方便

list(
  student1 = mean(exams$student1),
  student2 = mean(exams$student2),
  student3 = mean(exams$student3),
  student4 = mean(exams$student4),
  student5 = mean(exams$student5)
)

$student1: 79.6
$student2: 74.6
$student3: 78.7
$student4: 77
$student5: 67.9

`purrr`包#

对列表执行向量函数的运算

purrr::map(exams, mean)

$student1: 79.6
$student2: 74.6
$student3: 78.7
$student4: 77
$student5: 67.9

1 `map`函数#

map()函数的第一个参数是list或者vector，第二个参数是函数

函数f应用到list/vector的每个元素

于是输入的 list/vector 中的每个元素，都对应一个输出

最后，所有的输出元素，聚合成一个新的list

在我们这个例子，mean() 作用到每个学生的成绩向量，调用一次mean(), 返回一个数值，所以最终的结果是五个数值的列表。

# 也可用管道
exams %>% map(mean)

$student1: 79.6
$student2: 74.6
$student3: 78.7
$student4: 77
$student5: 67.9

2 `map`函数家族#

map_dbl()返回的是数值型的向量
- map_dbl()要求每个输出的元素必须是数值型
- 如果每个元素是数值型，map_dbl()会聚合所有元素构成一个原子型向量
map_df() 返回的结果是数据框

exams %>% map_dbl(mean)

student1: 79.6
student2: 74.6
student3: 78.7
student4: 77
student5: 67.9

exams %>% map_df(mean)

A tibble: 1 × 5
student1	student2	student3	student4	student5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
79.6	74.6	78.7	77	67.9

exams %>% 
  data.frame() %>% 
  map_df(mean)

A tibble: 1 × 5
student1	student2	student3	student4	student5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
79.6	74.6	78.7	77	67.9

3 `map`函数小结#

map函数第一个参数是向量或列表（数据框是列表的一种特殊形式，因此数据框也是可以的）
第二个参数是函数，这个函数会应用到列表的每一个元素，比如这里map函数执行过程如下

我们也可以根据需要，让map返回我们需要的数据格式, purrr也提供了方便的函数，具体如下

exams %>% map_df(var)  # 方差

A tibble: 1 × 5
student1	student2	student3	student4	student5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
178.0444	96.04444	235.1222	136.6667	211.6556

4 额外参数#

在sort()函数里添加参数 decreasing = TRUE改为降序
map很人性化，可以让函数的参数直接跟随在函数名之后,map()会自动的传递给函数。

map(exams, sort) # 默认升序
map(exams, sort, decreasing=TRUE) # 改为降序

$student1

62
67
68
70
74
81
88
89
98
99

$student2

57
66
72
73
73
75
76
76
84
94

$student3

54
57
65
77
82
85
86
87
96
98

$student4

55
65
70
71
76
81
86
86
90
90

$student5

51
55
57
62
62
65
72
74
81
100

$student1

99
98
89
88
81
74
70
68
67
62

$student2

94
84
76
76
75
73
73
72
66
57

$student3

98
96
87
86
85
82
77
65
57
54

$student4

90
90
86
86
81
76
71
70
65
55

$student5

100
81
74
72
65
62
62
57
55
51

5 匿名函数#

我们也可以自定义函数。比如我们这里定义了将向量中心化的函数（先求出10次考试的平均值，然后每次考试成绩去减这个平均值）

my_fun <- function(x){
    x - mean(x)
}
exams %>% map_df(my_fun)

A tibble: 10 × 5
student1	student2	student3	student4	student5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
9.4	0.4	-24.7	-12	-10.9
-9.6	-17.6	-13.7	-7	-12.9
-11.6	1.4	3.3	-6	32.1
18.4	19.4	6.3	13	-2.9
-5.6	-1.6	17.3	4	4.1
1.4	1.4	8.3	9	13.1
19.4	-1.6	7.3	-22	6.1
8.4	-2.6	-1.7	13	-5.9
-17.6	9.4	19.3	-1	-5.9
-12.6	-8.6	-21.7	9	-16.9

我们也可以不用命名函数，而使用匿名函数。匿名函数顾名思义，就是没有名字的函数，

匿名函数直接放在map()函数中

exams %>% 
  map_df(function(x){x - mean(x)})

A tibble: 10 × 5
student1	student2	student3	student4	student5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
9.4	0.4	-24.7	-12	-10.9
-9.6	-17.6	-13.7	-7	-12.9
-11.6	1.4	3.3	-6	32.1
18.4	19.4	6.3	13	-2.9
-5.6	-1.6	17.3	4	4.1
1.4	1.4	8.3	9	13.1
19.4	-1.6	7.3	-22	6.1
8.4	-2.6	-1.7	13	-5.9
-17.6	9.4	19.3	-1	-5.9
-12.6	-8.6	-21.7	9	-16.9

还可以更加偷懒，用~代替function()，但代价是参数必须是规定的写法，比如.x

exams %>% map_df(~.x - mean(.x))

A tibble: 10 × 5
student1	student2	student3	student4	student5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
9.4	0.4	-24.7	-12	-10.9
-9.6	-17.6	-13.7	-7	-12.9
-11.6	1.4	3.3	-6	32.1
18.4	19.4	6.3	13	-2.9
-5.6	-1.6	17.3	4	4.1
1.4	1.4	8.3	9	13.1
19.4	-1.6	7.3	-22	6.1
8.4	-2.6	-1.7	13	-5.9
-17.6	9.4	19.3	-1	-5.9
-12.6	-8.6	-21.7	9	-16.9

有时候，程序员觉得x还是有点多余，于是更够懒一点，只用.，也是可以的

exams %>% map_df(~. - mean(.))

A tibble: 10 × 5
student1	student2	student3	student4	student5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
9.4	0.4	-24.7	-12	-10.9
-9.6	-17.6	-13.7	-7	-12.9
-11.6	1.4	3.3	-6	32.1
18.4	19.4	6.3	13	-2.9
-5.6	-1.6	17.3	4	4.1
1.4	1.4	8.3	9	13.1
19.4	-1.6	7.3	-22	6.1
8.4	-2.6	-1.7	13	-5.9
-17.6	9.4	19.3	-1	-5.9
-12.6	-8.6	-21.7	9	-16.9

~ 告诉 map() 后面跟随的是一个匿名函数，. 对应函数的参数，可以认为是一个占位符，等待传送带的student1、student2到student5 依次传递到函数机器。

如果熟悉匿名函数的写法，会增强代码的可读性

exams %>% 
  map_df(~length(.[. > 80]))

A tibble: 1 × 5
student1	student2	student3	student4	student5
<int>	<int>	<int>	<int>	<int>
5	2	6	5	2

总之，有三种方法将函数传递给map()

直接传递

map(.x, mean, na.rm=TRUE)

Error in vctrs_vec_compat(.x, .purrr_user_env): 找不到对象'.x'
Traceback:

1. map(.x, mean, na.rm = TRUE)
2. map_("list", .x, .f, ..., .progress = .progress)
3. vctrs_vec_compat(.x, .purrr_user_env)

匿名函数

map(.x, funcition(.x){mean(.x, na.rm=TRUE)})

Error in parse(text = x, srcfile = src): <text>:1:22: 意外的'{'
1: map(.x, funcition(.x){
                         ^
Traceback:

使用 ~

function(.x){.x*2}

~.x*2

map(.x, ~mean(.x, na.rm=TRUE))

function (.x) 
{
    .x * 2
}

~.x * 2

Error in vctrs_vec_compat(.x, .purrr_user_env): 找不到对象'.x'
Traceback:

1. map(.x, ~mean(.x, na.rm = TRUE))
2. map_("list", .x, .f, ..., .progress = .progress)
3. vctrs_vec_compat(.x, .purrr_user_env)

在`dplyr`函数中的运用`map`#

1 在`Tibble`中#

Tibble本质上是向量构成的列表，因此tibble也适用map。假定有tibble如下

tb <- 
  tibble(
    col_1 = c(1, 2, 3),
    col_2 = c(100, 200, 300),
    col_3 = c(0.1, 0.2, 0.3)
  )

map()中的函数f，可以作用到每一列

map_df(tb, median)

A tibble: 1 × 3
col_1	col_2	col_3
<dbl>	<dbl>	<dbl>
2	200	0.2

在比如，找出企鹅数据中每列缺失值NA的数量

palmerpenguins::penguins %>% 
  map_df(~sum(is.na(.)))

A tibble: 1 × 8
species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
0	0	2	2	2	2	11	0

2 在`col-column`中#

如果想显示列表中每个元素的长度，可以这样写

tibble(
  x = list(1, 2:3, 4:6)
) %>% 
  mutate(length = purrr::map_int(x, length))

A tibble: 3 × 2
x	length
<list>	<int>
1	1
2, 3	2
4, 5, 6	3

用于各种函数，比如产生随机数

tibble(
  x = c(3, 5, 6)
) %>% 
  mutate(r = purrr::map(x, ~rnorm(., mean= 0, sd = 1)))

A tibble: 3 × 2
x	r
<dbl>	<list>
3	-0.7442620, 0.2733458, 0.1971244
5	-1.6398005, -0.1076375, 0.2406252, 0.1272739, 1.0788640
6	-1.67253048, -0.69551409, -0.57144249, 1.36591820, 0.07352168, -1.36195263

用于建模

mtcars %>% 
  group_by(cyl) %>% 
  nest() %>% 
  mutate(model = purrr::map(data, ~ lm(mpg ~ wt, data = .))) %>% 
  mutate(result = purrr::map(model, ~ broom::tidy(.))) %>% 
  unnest(result)

A grouped_df: 6 × 8
cyl	data	model	term	estimate	std.error	statistic	p.value
<dbl>	<list>	<list>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	(Intercept)	28.408845	4.1843688	6.789278	1.054844e-03
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	wt	-2.780106	1.3349173	-2.082605	9.175766e-02
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	(Intercept)	39.571196	4.3465820	9.103980	7.771511e-06
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	wt	-5.647025	1.8501185	-3.052251	1.374278e-02
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	(Intercept)	23.868029	3.0054619	7.941551	4.052705e-06
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	wt	-2.192438	0.7392393	-2.965803	1.179281e-02

# midify会修改原始数据，推荐使用map
exams %>% map_df(~ . - mean(.))

exams %>% modify(~ . - mean(.))

A tibble: 10 × 5
student1	student2	student3	student4	student5
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
9.4	0.4	-24.7	-12	-10.9
-9.6	-17.6	-13.7	-7	-12.9
-11.6	1.4	3.3	-6	32.1
18.4	19.4	6.3	13	-2.9
-5.6	-1.6	17.3	4	4.1
1.4	1.4	8.3	9	13.1
19.4	-1.6	7.3	-22	6.1
8.4	-2.6	-1.7	13	-5.9
-17.6	9.4	19.3	-1	-5.9
-12.6	-8.6	-21.7	9	-16.9

$student1

9.40000000000001
-9.59999999999999
-11.6
18.4
-5.59999999999999
1.40000000000001
19.4
8.40000000000001
-17.6
-12.6

$student2

0.400000000000006
-17.6
1.40000000000001
19.4
-1.59999999999999
1.40000000000001
-1.59999999999999
-2.59999999999999
9.40000000000001
-8.59999999999999

$student3

-24.7
-13.7
3.3
6.3
17.3
8.3
7.3
-1.7
19.3
-21.7

$student4

-12
-7
-6
13
4
9
-22
13
-1
9

$student5

-10.9
-12.9
32.1
-2.90000000000001
4.09999999999999
13.1
6.09999999999999
-5.90000000000001
-5.90000000000001
-16.9

函数式编程2#

事实上，purrr()家族还有其它map()函数，可以在多个向量中迭代。也就说，同时接受多个向量的元素，并行计算。比如，map2()函数可以处理两个向量，而pmap()函数可以处理更多向量。

library(tidyverse)

`map2()`#

map2()函数和map()函数类似，不同在于map2()接受两个的向量，这两个向量必须是等长在map()函数使用匿名函数，可以用 . 代表输入向量的每个元素。在map2()函数， .不够用，所有需要需要用 .x 代表第一个向量的元素，.y代表第二个向量的元素

x <- c(1,2,3)
y <- c(4,5,6)

map2(x, y, ~.x + .y)

5
7
9

tibble的每一列都是向量，所以可以把map2()放在mutate()函数内部，对tibble的多列同时迭代

df <- tibble(
  a = c(1, 2, 3),
  b = c(4, 5, 6)
)

df %>% 
  mutate(min = map2(a, b, ~min(.x, .y)))

A tibble: 3 × 3
a	b	min
<dbl>	<dbl>	<list>
1	4	1
2	5	2
3	6	3

也可以简写

df %>% 
  mutate(min = map2_dbl(a, b, min))

A tibble: 3 × 3
a	b	min
<dbl>	<dbl>	<dbl>
1	4	1
2	5	2
3	6	3

mutate() 是column-operation，即提取数据框一列作为向量，传递到mutate中，map2_dbl()返回的也是一个等长的向量。

因此，也可以用rowwise()逐行应用函数

df %>% 
  rowwise() %>% 
  mutate(min = min(a, b)) %>% 
  ungroup()

A tibble: 3 × 3
a	b	min
<dbl>	<dbl>	<dbl>
1	4	1
2	5	2
3	6	3

`pmap()`#

没有map3()或者map4()函数，只有 pmap() 函数可用(p 的意思是 parallel)

purrr::pmap()函数稍微有点不一样的地方:

map()和map2()函数，指定传递给函数f的向量，向量各自放在各自的位置上
pmap()需要将传递给函数的向量名，先装入一个list()中, 再传递给函数f 翻转列表的图示，参数的传递关系看地更清楚。

事实上，map2()是pmap()的一种特殊情况

map2_dbl(x, y, min)

1
2
3

pmap_dbl(list(x, y), min)

1
2
3

1 用在`tibble`#

tibble本质上就是list，这种结构就是pmap()所需要的，因此，直接应用函数即可。

tibble(
  a = c(50, 60, 70),
  b = c(10, 90, 40),
  c = c(1, 105, 200)
) %>% 
  pmap_dbl(min)

1
60
40

2 匿名函数#

pmap()可以接受多个向量，因此在pmap()中使用匿名函数，就需要一种新的方法来标识每个向量。

由于向量是多个，因此不再用.x， .y，而是用..1, ..2, ..3 分别代表第一个向量、第二个向量和第三个向量。

pmap(
  list(1:5, 5:1, 2), ~..1 + ..2 + ..3
)

8
8
8
8
8

3 命名函数#

runif()产生均匀分布随机数

params <- tibble::tribble(
  ~ n, ~ min, ~ max,
   1L,     0,     1,
   2L,    10,   100,
   3L,   100,  1000
)
pmap(params, ~runif(n = ..1, min = ..2, max = ..3))

0.0769681525416672
1. 28.0839628400281
2. 41.5693232929334
1. 199.041049205698
2. 191.574636357836
3. 512.634686636738

如果提供给pmap()的.f 是命名函数，比如runif(n, min = , max = )，它有三个参数 n, min, max，而我们输入的列表刚好也有三个同名的元素，那么他们会自动匹配，代码因此变得更加简练

pmap(params, runif)

0.959976142272353
1. 13.1245567044243
2. 59.6250989451073
1. 590.096804616041
2. 511.394972563721
3. 332.652951683849

当然，这里需要注意的是

输入列表的元素，其个数要与函数的参数个数一致
输入列表的元素，其变量名也要与函数的参数名一致

其他`purrr`函数#

1 Map functions that output tibbles#

接着介绍purrr宏包的其他函数。 map()家族除了返回list和atomic vector 外，map_df(), map_dfr() 和map_dfc()还可以返回tibble。

这个过程，好比生产线上的工人把输入的列表元素依次转换成一个个tibble，最后归集一个大的tibble。在归集成一个大的tibble的时候，有两种方式，

竖着堆积，map_dfr()(r for rows)
并排堆放map_dfc()(c for columns)

2 Walk and friends#

walk()函数与map()系列函数类似，但应用场景不同，map()在于执行函数操作，而walk() 保存记录数据（比如print(),write.csv(), ggsave()），常用于保存数据和生成图片。比如我们用map()生成系列图片，

plot_rnorm <- function(sd){
    tibble(x = rnorm(n = 5000, mean = 0, sd = sd)) %>% 
      ggplot(aes(x)) +
      geom_histogram(bins = 40) +
      geom_vline(xintercept = 0, color = "blue")
}

plots <- 
  c(5, 1, 9) %>% 
  map(plot_rnorm)

plots %>% 
  walk(print)

../_images/23aec9776f167d2ab6c415584f28f6ab106d87d350cf48e8b9c180a2819009eb.png

../_images/98dd5b91d00f118f57df1253c46c77633c826a9ea52679670a5ce162baf8756f.png

../_images/63263972200cd580b497b7b912b403672d2cd06dfb792a033d795444e926c7a0.png

map()函数是一定要返回列表的，但walk()看上去函数没有返回值，实际上它返回的就是它的输入，只是用户不可见而已。这样的设计很有用，尤其在管道操作中，我们可以统计中，用walk()保存中间计算的结果或者生成图片，然后若无其事地继续管道（因为walk()返回值，就是输入walk的值），保持计算的连贯。

因子型变量_数据框_函数式编程purrr包

Contents

因子型变量_数据框_函数式编程purrr包#

什么是因子#

创建因子#

调整因子顺序#

应用#

fct_reorder()#

fct_rev()#

fct_relevel()#

可视化中应用#

简单数据框#

tidyverse 家族#

人性化的tibble#

tibble 与 data.frame#

tibble数据操作#

1 创建tibble#

2 转换成tibble类型#

3 tibble简单操作#

4 有用的函数lst#

5 有用的函数enframe#

6 有用的函数deframe#

7 读取文件#

关于行名#

修复列名#

List-columns(列表列)#

nested tibble#

1 creating#

tidyr::nest()创建

1 creating#

tidyr::summarise(list())创建#

1 creating#

dplyr::mutate()创建#

2 Unnesting#

Manipulating#

函数式编程1——purrr#

向量化运算#

列表#

列表 vs 向量#

purrr包#

1 map函数#

2 map函数家族#

3 map函数小结#

4 额外参数#

5 匿名函数#

在dplyr函数中的运用map#

1 在Tibble中#

2 在col-column中#

函数式编程2#

map2()#

pmap()#

1 用在tibble#

2 匿名函数#

3 命名函数#

其他purrr函数#

1 Map functions that output tibbles#

2 Walk and friends#

`fct_reorder()`#

`fct_rev()`#

`fct_relevel()`#

`tidyverse` 家族#

人性化的`tibble`#

`tibble`数据操作#

1 创建`tibble`#

2 转换成`tibble`类型#

3 `tibble`简单操作#

4 有用的函数`lst`#

5 有用的函数`enframe`#

6 有用的函数`deframe`#

`List-columns`(列表列)#

`nested tibble`#

1 `creating`#

`tidyr::nest()`创建

`tidyr::summarise(list())`创建#

`dplyr::mutate()`创建#

函数式编程1——`purrr`#

`purrr`包#

1 `map`函数#

2 `map`函数家族#

3 `map`函数小结#

在`dplyr`函数中的运用`map`#

1 在`Tibble`中#

2 在`col-column`中#

`map2()`#

`pmap()`#

1 用在`tibble`#

其他`purrr`函数#